





'''simple docstring'''




from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

# General docstring
_SCREAMING_SNAKE_CASE							     =			"RegNetConfig"

# Base docstring
_SCREAMING_SNAKE_CASE							     =			"facebook/regnet-y-040"
_SCREAMING_SNAKE_CASE							     =			[1, 1088, 7, 7]

# Image classification docstring
_SCREAMING_SNAKE_CASE							     =			"facebook/regnet-y-040"
_SCREAMING_SNAKE_CASE							     =			"tabby, tabby cat"

_SCREAMING_SNAKE_CASE							     =			[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 			_lowerCAmelCase		(			tf.keras.layers.Layer	):






     """simple docstring"""






     def __init__(   self							:			Dict   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Union[str, Any] = 3   ,       __snake_case							:			Optional[int] = 1   ,       __snake_case							:			List[Any] = 1   ,       __snake_case							:			List[Any] = "relu"   ,       **__snake_case							:			Union[str, Any]   ,       )->	str:
        super().__init__(**__snake_case						)
        # The padding and conv has been verified in
        # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
        snake_case 						=   tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2						)
        snake_case 						=   tf.keras.layers.ConvaD(
            filters=__snake_case   ,       kernel_size=__snake_case   ,       strides=__snake_case   ,       padding="""VALID"""   ,       groups=__snake_case   ,       use_bias=__snake_case   ,       name="""convolution"""   ,       )
        snake_case 						=   tf.keras.layers.BatchNormalization(epsilon=1e-5   ,       momentum=0.9   ,       name="""normalization"""						)
        snake_case 						=   ACTaFN[activation] if activation is not None else tf.identity



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			int						)->	int:
        snake_case 						=   self.convolution(self.padding(__snake_case						)						)
        snake_case 						=   self.normalization(__snake_case						)
        snake_case 						=   self.activation(__snake_case						)
        return hidden_state




class 			_lowerCAmelCase		(			tf.keras.layers.Layer	):






     """simple docstring"""






     def __init__(   self							:			str   ,       __snake_case							:			Optional[Any]   ,       **__snake_case							:			Optional[int]						)->	List[Any]:
        super().__init__(**__snake_case						)
        snake_case 						=   config.num_channels
        snake_case 						=   TFRegNetConvLayer(
            out_channels=config.embedding_size   ,       kernel_size=3   ,       stride=2   ,       activation=config.hidden_act   ,       name="""embedder"""   ,       )



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			Dict						)->	Optional[Any]:
        snake_case 						=   shape_list(__snake_case						)[1]
        if tf.executing_eagerly() and num_channels != self.num_channels:
           raise ValueError(
               """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""						)

        # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
        # So change the input format from `NCHW` to `NHWC`.
        # shape = (batch_size, in_height, in_width, in_channels=num_channels)
        snake_case 						=   tf.transpose(__snake_case   ,       perm=(0, 2, 3, 1)						)
        snake_case 						=   self.embedder(__snake_case						)
        return hidden_state




class 			_lowerCAmelCase		(			tf.keras.layers.Layer	):






     """simple docstring"""






     def __init__(   self							:			Dict   ,       __snake_case							:			str   ,       __snake_case							:			Dict = 2   ,       **__snake_case							:			Any						)->	List[str]:
        super().__init__(**__snake_case						)
        snake_case 						=   tf.keras.layers.ConvaD(
            filters=__snake_case   ,       kernel_size=1   ,       strides=__snake_case   ,       use_bias=__snake_case   ,       name="""convolution"""						)
        snake_case 						=   tf.keras.layers.BatchNormalization(epsilon=1e-5   ,       momentum=0.9   ,       name="""normalization"""						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			Any   ,       __snake_case							:			int = False						)->	tf.Tensor:
        return self.normalization(self.convolution(__snake_case						)   ,       training=__snake_case						)




class 			_lowerCAmelCase		(			tf.keras.layers.Layer	):






     """simple docstring"""






     def __init__(   self							:			Any   ,       __snake_case							:			Any   ,       __snake_case							:			int   ,       **__snake_case							:			Optional[Any]						)->	Any:
        super().__init__(**__snake_case						)
        snake_case 						=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=__snake_case   ,       name="""pooler"""						)
        snake_case 						=   [
            tf.keras.layers.ConvaD(filters=__snake_case   ,       kernel_size=1   ,       activation="""relu"""   ,       name="""attention.0"""						),
            tf.keras.layers.ConvaD(filters=__snake_case   ,       kernel_size=1   ,       activation="""sigmoid"""   ,       name="""attention.2"""						),
        ]



     def      lowerCAmelCase			(   self							:			Dict   ,       __snake_case							:			List[Any]						)->	Any:
        # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
        snake_case 						=   self.pooler(__snake_case						)
        for layer_module in self.attention:
           snake_case 						=   layer_module(__snake_case						)
        snake_case 						=   hidden_state * pooled
        return hidden_state




class 			_lowerCAmelCase		(			tf.keras.layers.Layer	):






     """simple docstring"""






     def __init__(   self							:			Union[str, Any]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			List[Any] = 1   ,       **__snake_case							:			List[str]						)->	List[str]:
        super().__init__(**__snake_case						)
        snake_case 						=   in_channels != out_channels or stride != 1
        snake_case 						=   max(1   ,       out_channels // config.groups_width						)
        snake_case 						=   (
            TFRegNetShortCut(__snake_case   ,       stride=__snake_case   ,       name="""shortcut"""						)
            if should_apply_shortcut
            else tf.keras.layers.Activation("""linear"""   ,       name="""shortcut"""						)
        )
        # `self.layers` instead of `self.layer` because that is a reserved argument.
        snake_case 						=   [
            TFRegNetConvLayer(__snake_case   ,       kernel_size=1   ,       activation=config.hidden_act   ,       name="""layer.0"""						),
            TFRegNetConvLayer(
                __snake_case   ,       stride=__snake_case   ,       groups=__snake_case   ,       activation=config.hidden_act   ,       name="""layer.1"""						),
            TFRegNetConvLayer(__snake_case   ,       kernel_size=1   ,       activation=__snake_case   ,       name="""layer.2"""						),
        ]
        snake_case 						=   ACTaFN[config.hidden_act]



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			List[str]						)->	Dict:
        snake_case 						=   hidden_state
        for layer_module in self.layers:
           snake_case 						=   layer_module(__snake_case						)
        snake_case 						=   self.shortcut(__snake_case						)
        hidden_state += residual
        snake_case 						=   self.activation(__snake_case						)
        return hidden_state




class 			_lowerCAmelCase		(			tf.keras.layers.Layer	):






     """simple docstring"""






     def __init__(   self							:			List[Any]   ,       __snake_case							:			Dict   ,       __snake_case							:			Tuple   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Optional[Any] = 1   ,       **__snake_case							:			Optional[Any]						)->	List[str]:
        super().__init__(**__snake_case						)
        snake_case 						=   in_channels != out_channels or stride != 1
        snake_case 						=   max(1   ,       out_channels // config.groups_width						)
        snake_case 						=   (
            TFRegNetShortCut(__snake_case   ,       stride=__snake_case   ,       name="""shortcut"""						)
            if should_apply_shortcut
            else tf.keras.layers.Activation("""linear"""   ,       name="""shortcut"""						)
        )
        snake_case 						=   [
            TFRegNetConvLayer(__snake_case   ,       kernel_size=1   ,       activation=config.hidden_act   ,       name="""layer.0"""						),
            TFRegNetConvLayer(
                __snake_case   ,       stride=__snake_case   ,       groups=__snake_case   ,       activation=config.hidden_act   ,       name="""layer.1"""						),
            TFRegNetSELayer(__snake_case   ,       reduced_channels=int(round(in_channels / 4						)						)   ,       name="""layer.2"""						),
            TFRegNetConvLayer(__snake_case   ,       kernel_size=1   ,       activation=__snake_case   ,       name="""layer.3"""						),
        ]
        snake_case 						=   ACTaFN[config.hidden_act]



     def      lowerCAmelCase			(   self							:			Dict   ,       __snake_case							:			List[Any]						)->	Dict:
        snake_case 						=   hidden_state
        for layer_module in self.layers:
           snake_case 						=   layer_module(__snake_case						)
        snake_case 						=   self.shortcut(__snake_case						)
        hidden_state += residual
        snake_case 						=   self.activation(__snake_case						)
        return hidden_state




class 			_lowerCAmelCase		(			tf.keras.layers.Layer	):






     """simple docstring"""






     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			int   ,       __snake_case							:			List[Any]   ,       __snake_case							:			str = 2   ,       __snake_case							:			int = 2   ,       **__snake_case							:			Any						)->	List[Any]:
        super().__init__(**__snake_case						)

        snake_case 						=   TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
        snake_case 						=   [
            # downsampling is done in the first layer with stride of 2
            layer(__snake_case   ,       __snake_case   ,       __snake_case   ,       stride=__snake_case   ,       name="""layers.0"""						),
            *[layer(__snake_case   ,       __snake_case   ,       __snake_case   ,       name=f'''layers.{i+1}'''						) for i in range(depth - 1						)],
        ]



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			int						)->	str:
        for layer_module in self.layers:
           snake_case 						=   layer_module(__snake_case						)
        return hidden_state




class 			_lowerCAmelCase		(			tf.keras.layers.Layer	):






     """simple docstring"""






     def __init__(   self							:			Dict   ,       __snake_case							:			Optional[int]   ,       **__snake_case							:			List[Any]						)->	List[str]:
        super().__init__(**__snake_case						)
        snake_case 						=   []
        # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
        self.stages.append(
            TFRegNetStage(
                __snake_case   ,       config.embedding_size   ,       config.hidden_sizes[0]   ,       stride=2 if config.downsample_in_first_stage else 1   ,       depth=config.depths[0]   ,       name="""stages.0"""   ,       )						)
        snake_case 						=   zip(config.hidden_sizes   ,       config.hidden_sizes[1:]						)
        for i, ((in_channels, out_channels), depth) in enumerate(zip(__snake_case   ,       config.depths[1:]						)						):
           self.stages.append(TFRegNetStage(__snake_case   ,       __snake_case   ,       __snake_case   ,       depth=__snake_case   ,       name=f'''stages.{i+1}'''						)						)



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			List[Any] = False   ,       __snake_case							:			List[str] = True						)->	TFBaseModelOutputWithNoAttention:
        snake_case 						=   () if output_hidden_states else None

        for stage_module in self.stages:
           if output_hidden_states:
              snake_case 						=   hidden_states + (hidden_state,)

           snake_case 						=   stage_module(__snake_case						)

        if output_hidden_states:
           snake_case 						=   hidden_states + (hidden_state,)

        if not return_dict:
           return tuple(v for v in [hidden_state, hidden_states] if v is not None						)

        return TFBaseModelOutputWithNoAttention(last_hidden_state=__snake_case   ,       hidden_states=__snake_case						)




@keras_serializable
class 			_lowerCAmelCase		(			tf.keras.layers.Layer	):






     """simple docstring"""






     snake_case_						      =   RegNetConfig



     def __init__(   self							:			List[Any]   ,       __snake_case							:			List[Any]   ,       **__snake_case							:			Dict						)->	List[str]:
        super().__init__(**__snake_case						)
        snake_case 						=   config
        snake_case 						=   TFRegNetEmbeddings(__snake_case   ,       name="""embedder"""						)
        snake_case 						=   TFRegNetEncoder(__snake_case   ,       name="""encoder"""						)
        snake_case 						=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=__snake_case   ,       name="""pooler"""						)



     @unpack_inputs
     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			Tuple   ,       __snake_case							:			Union[str, Any] = None   ,       __snake_case							:			Union[str, Any] = None   ,       __snake_case							:			str = False   ,       )->	TFBaseModelOutputWithPoolingAndNoAttention:
        snake_case 						=   (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        snake_case 						=   return_dict if return_dict is not None else self.config.use_return_dict

        snake_case 						=   self.embedder(__snake_case   ,       training=__snake_case						)

        snake_case 						=   self.encoder(
            __snake_case   ,       output_hidden_states=__snake_case   ,       return_dict=__snake_case   ,       training=__snake_case						)

        snake_case 						=   encoder_outputs[0]
        snake_case 						=   self.pooler(__snake_case						)

        # Change to NCHW output format have uniformity in the modules
        snake_case 						=   tf.transpose(__snake_case   ,       perm=(0, 3, 1, 2)						)
        snake_case 						=   tf.transpose(__snake_case   ,       perm=(0, 3, 1, 2)						)

        # Change the other hidden state outputs to NCHW as well
        if output_hidden_states:
           snake_case 						=   tuple([tf.transpose(__snake_case   ,       perm=(0, 3, 1, 2)						) for h in encoder_outputs[1]]						)

        if not return_dict:
           return (last_hidden_state, pooled_output) + encoder_outputs[1:]

        return TFBaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=__snake_case   ,       pooler_output=__snake_case   ,       hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states   ,       )




class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   RegNetConfig
     snake_case_						      =   "regnet"
     snake_case_						      =   "pixel_values"



     @property
     def      lowerCAmelCase			(   self							:			str						)->	List[Any]:
        return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)   ,       dtype=tf.floataa						)}




_SCREAMING_SNAKE_CASE							     =			r"\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"

_SCREAMING_SNAKE_CASE							     =			r"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."							,     A__							,     )
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     def __init__(   self							:			List[Any]   ,       __snake_case							:			Optional[Any]   ,       *__snake_case							:			Optional[int]   ,       **__snake_case							:			Optional[int]						)->	Optional[int]:
        super().__init__(__snake_case   ,       *__snake_case   ,       **__snake_case						)
        snake_case 						=   TFRegNetMainLayer(__snake_case   ,       name="""regnet"""						)



     @unpack_inputs
     @add_start_docstrings_to_model_forward(__snake_case						)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC   ,       output_type=__snake_case   ,       config_class=_CONFIG_FOR_DOC   ,       modality="""vision"""   ,       expected_output=_EXPECTED_OUTPUT_SHAPE   ,       )
     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			Any   ,       __snake_case							:			Tuple = None   ,       __snake_case							:			str = None   ,       __snake_case							:			int=False   ,       )->	Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
        snake_case 						=   (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        snake_case 						=   return_dict if return_dict is not None else self.config.use_return_dict

        snake_case 						=   self.regnet(
            pixel_values=__snake_case   ,       output_hidden_states=__snake_case   ,       return_dict=__snake_case   ,       training=__snake_case   ,       )
        if not return_dict:
           return (outputs[0],) + outputs[1:]

        return TFBaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=outputs.last_hidden_state   ,       pooler_output=outputs.pooler_output   ,       hidden_states=outputs.hidden_states   ,       )




@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "							,     A__							,     )
class 			_lowerCAmelCase		(			A__							,     A__	):






     """simple docstring"""






     def __init__(   self							:			int   ,       __snake_case							:			List[str]   ,       *__snake_case							:			List[str]   ,       **__snake_case							:			Dict						)->	List[Any]:
        super().__init__(__snake_case   ,       *__snake_case   ,       **__snake_case						)
        snake_case 						=   config.num_labels
        snake_case 						=   TFRegNetMainLayer(__snake_case   ,       name="""regnet"""						)
        # classification head
        snake_case 						=   [
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(config.num_labels   ,       name="""classifier.1"""						) if config.num_labels > 0 else tf.identity,
        ]



     @unpack_inputs
     @add_start_docstrings_to_model_forward(__snake_case						)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT   ,       output_type=__snake_case   ,       config_class=_CONFIG_FOR_DOC   ,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,       )
     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			Optional[Any] = None   ,       __snake_case							:			Optional[Any] = None   ,       __snake_case							:			Tuple = None   ,       __snake_case							:			Optional[int] = None   ,       __snake_case							:			str=False   ,       )->	Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        snake_case 						=   (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        snake_case 						=   return_dict if return_dict is not None else self.config.use_return_dict

        snake_case 						=   self.regnet(
            __snake_case   ,       output_hidden_states=__snake_case   ,       return_dict=__snake_case   ,       training=__snake_case						)

        snake_case 						=   outputs.pooler_output if return_dict else outputs[1]

        snake_case 						=   self.classifier[0](__snake_case						)
        snake_case 						=   self.classifier[1](__snake_case						)

        snake_case 						=   None if labels is None else self.hf_compute_loss(labels=__snake_case   ,       logits=__snake_case						)

        if not return_dict:
           snake_case 						=   (logits,) + outputs[2:]
           return ((loss,) + output) if loss is not None else output

        return TFSequenceClassifierOutput(loss=__snake_case   ,       logits=__snake_case   ,       hidden_states=outputs.hidden_states						)







'''simple docstring'''


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/blenderbot_small-90M": 5_12,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   BlenderbotSmallTokenizer







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     List[Any]:
     super().__init__(
         ByteLevelBPETokenizer(
             vocab=UpperCamelCase   ,				merges=UpperCamelCase   ,				add_prefix_space=UpperCamelCase   ,				trim_offsets=UpperCamelCase   ,				)   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       add_prefix_space







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[Any]:
     __a								=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



